Array-based polymer-phage biosensors for detection and differentiation of bacteria

Abstract

Pathogenic bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), pose significant challenges to public health due to their resistance to conventional antibiotics. Early and accurate identification of bacterial species and discrimination of their strains is critical for guiding effective treatments and infection control. In this study, we develop a polymer-phage sensor platform that integrates polymer-based fluorescence sensing with phage-host specificity for bacterial identification. The sensor successfully differentiates three bacterial species (S. aureus, E. coli, and B. subtilis) and closely related strains of S. aureus (methicillin-sensitive Staphylococcus aureus (MSSA) and MRSA) with high classification accuracy (94-100%) and correct unknown identification rates (94-100%) under optimized conditions. By leveraging phage-host interactions and polymer binding properties, the polymer-phage sensor overcomes the limitations of traditional "lock-and-key" biosensors, offering enhanced specificity and reliability. This platform's rapid response time and adaptability make it a promising tool for clinical diagnostics and public health applications, particularly in combating antibiotic-resistant bacteria.

Fig. 1. Design and working principle of the polymer-phage sensor. A). Chemical structure of poly(oxanorborneneimide) (PONI), functionalized with guanidine groups and a pyrene fluorophore. The schematic illustrates the interaction between the bacteriophage and the polymer, where the positively charged guanidine groups facilitate electrostatic interactions with the negatively charged phage capsid, creating the polymer-phage sensor. B). Transmission electron microscopy (TEM) images of individual phage K and polymer-phage assemblies. C). Schematic representation of the sensing mechanism. Interaction of the polymer–phage sensor with different bacterial species generates distinct fluorescence response patterns, enabling bacterial discrimination through multivariate statistical analysis.

Fig. 2. Differentiation of different bacterial species after 1-hour using polymer-only and polymer-phage sensors. A–C). Fluorescence signals of the four sensor channels normalized to the sensor only (I/I₀). D–F). LDA plot of the first two canonical scores with 95% confidence ellipses. All of the experiments include six biological repetitions.

Fig. 3. Comparison of polymer-only and polymer-phage sensors in differentiating Gram positive bacteria, including different strains of the same species, after 30 minutes. A and D). Fluorescence signals of the four sensor channels were normalized to the sensor only. Fluorescence intensities of each treatment group were obtained at 30 min and normalized against the sensor only. All the experiments included eight biological repetitions. B and E). LDA of the first two canonical score plots of the fluorescence response patterns. All the experiments included eight biological repetitions. C and F). Classification accuracy and correct unknown identification of B. subtilis and three different strains of S. aureus after 30 min and 60 min.